RBQM Ops 2022 - How do we identify and asses patients at risk?
How do we identify and assess patients at risk?
subject, AEs, patients, adverse events, reporting, model, study, trial, treatment, risks, medical history, age, number, serious adverse event, site, chest pain, expect, assessments, prediction, statistician, Sponsor, CRO
The next topic is how do we identify and assess patients at risk? And our speaker is Laura Illingworth, TRIs in house statistician. And those of you who joined us yesterday will know that law has very experienced and has worked extensively with our Sponsor and CRO clients to develop this statistical model visualizations that we use in the OPRA platform. So, again, Laura, welcome to you and we look forward to hearing your views. Thank you.
Thanks for the introduction, Duncan. I'm Laura Illingworth and I work here at TRI and oversee the statistical parts of OPRA. And in this session, we're going to look at how do we identify and assess patients at risk. I’d just like to take this time to say that these views are my own and aren't affiliated with TRI.
Right, let's get moving. So, I've grouped patient risks into two groups, we have the clinical research risks and the safety risks. And so, some of the risks which we could find within the clinical research, could be patients withdrawing from the trial assessments, not completed or performed correctly. Treatment scheduled not adhered to, the subject being illegible for the trial, or repeated subjects. What I mean by that is one subject enrolling twice into the study. So, safety risks.
And then within safety risks, we have adverse events, and that's where a subject might be reporting lots of adverse events. And their health is at higher risk being in study. Or they might be under reporting the number of AEs which they're actually having. And we could also see this occurring, perhaps within serious adverse events, however, would hope that patients would be seeking medical treatment for a serious adverse event. Assessment not being completed, and therefore the patient could potentially be put at risk, especially if the treatment is known to have side effects, and these assessments aren't being completed.
We can also have on a subject level, poor recall of answers, or the quality of the data coming in. And this might be especially important in decentralised clinical trials where the patient themselves would be trying to answer the medical questions, and perhaps forgetting about their medical history. And this, again, could lead to subjects being enrolled onto the study who perhaps shouldn't be there.
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